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Recent chassis testing of hybrid buses demonstrated the potential of hybrid technology to reduce emissions and raise fuel economy relative to conventional buses. However, hybrid buses represent a certification quandary because the engines must be certified using the accepted Federal Test Procedure (FTP), without regard for benefits that may arise from less transient engine operation. Actual engine operating data from series configuration hybrid buses were analyzed to determine the envelopes of torque and speeds covered by the engine. Transient engine operation was also considered in terms of rates of change of torque, power and speed. These measures did not compare closely with similar measures computed from the FTP because the series hybrid engines explored a more structured zone of operation than the FTP implied and because the FTP represented more transient operation.

Emissions tests for heavy -duty diesel-fueled engines and vehicles are normally performed using engine dynamometers and chassis dynamometers, respectively, with laboratory grade gaseous concentration measurement analyzers and supporting test equipment. However, a considerable effort has been recently expended on developing in-use, on-board tools to measure brake-specific emissions from heavy -duty vehicles with the highest degree of accuracy and precision. This alternative testing methodology would supplement the emissions data that is collected from engine and chassis dynamometer tests. The on-board emissions testing methodology entails actively recording emissions and vehicle operating parameters (engine speed and load, vehicle speed etc.) from vehicles while they are operating on the road. This paper focuses on in-use measurements of NOX with zirconium oxide sensors and other portable NOX detectors.

Today's computer-based vehicle operation simulators use engine speed, engine torque, and lookup tables to predict emissions during a driving simulation [1]. This approach is used primarily for light and medium-duty vehicles, with large discrepancies inherently due to the lack of transient engine emissions data and inaccurate emissions prediction methods [2]. West Virginia University (WVU) has developed an artificial neural network (ANN) based emissions model for incorporation into the ADvanced VehIcle SimulatOR (ADVISOR) software package developed by the National Renewable Energy Laboratory (NREL). Transient engine dynamometer tests were conducted to obtain training data for the ANN. The ANN was trained to predict carbon dioxide (CO2) and oxides of nitrogen (NOx) emissions based on engine speed, torque, and their representative first and second derivatives over various time ranges.

Gaseous and particulate emissions from heavy-duty vehicles are affected by fuel types, vehicle/engine parameters, driving characteristics, and environmental conditions. Transient chassis tests were conducted on twenty-six heavy-duty vehicles fueled with methanol, compressed natural gas (CNG), #1 diesel, and #2 diesel, using West Virginia University (WVU) Transportable Heavy-Duty Vehicle Emissions Testing Laboratory. The vehicles were operated on the central business district (CBD) testing cycle, and regulated emissions of carbon monoxide (CO), total hydrocarbon (HC), nitrogen oxides (NOx), and particulate matter (PM) were measured. Comparisons of regulated emissions results revealed that the vehicles powered on methanol and CNG produced much lower particulate emissions than the conventionally fueled vehicles.